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Analysis of Respiratory EMG Signals for Cough Prediction

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XXVII Brazilian Congress on Biomedical Engineering (CBEB 2020)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 83))

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Abstract

Respiratory muscle weakness can be one of the aftereffects due to cervical or high thoracic spinal cord injury (SCI). This problem affects the organism's capacity to maintain the correct levels of oxygen, and the cough events start to be difficult to perform. Studies have demonstrated that the application of synchronized Transcutaneous Functional Electrical Stimulation (TFES) to the respiratory muscles, diaphragm and abdomen, enhanced their resistance and strength. So, this study aims to evaluate if electromyography (EMG) sensors positioned on the sternocleidomastoid (SCM), omohyoid (OM), and external oblique (EO) muscles can be used to help in automatic cough prediction for a future TFES synchronization system. Voluntary coughs of nineteen volunteers were recorded using three EMG sensors and their potential use in cough prediction was evaluated. Results show that EO muscle presents better results (55 ± 31.37 V) in amplitude variations compared to the other ones. However, it is still not possible to standardize one single muscle for prediction due to the variation of activation in each person. Also, the use of EMG sensors can contribute significantly to cough prediction systems considering the muscle response can be perceived well before the sound.

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Acknowledgements

The authors thank CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico), Brazil, for scholarships and financial support.

The author TDC thanks for the scholarship received from CAPES for the PDSE program (process number: 88881.131558/2016-01).

Conflict of Interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Universidade Tecnológica Federal do Paraná, Curitiba, Paraná, Brazil

    T. D. Costa & P. Nohama

  2. Pontifícia Universidade Católica do Paraná, Rua Imaculada Conceição, 1155, Prado Velho, ZIP 80215-901, Curitiba, Paraná, Brazil

    T. Z. Zanella, C. S. Cristino, Guilherme Nunes Nogueira-Neto & P. Nohama

  3. California State University Long Beach, Long Beach, CA, USA

    T. D. Costa & C. Druzgalski

Authors
  1. T. D. Costa
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  2. T. Z. Zanella
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  3. C. S. Cristino
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  4. C. Druzgalski
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  5. Guilherme Nunes Nogueira-Neto
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  6. P. Nohama
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Corresponding author

Correspondence to Guilherme Nunes Nogueira-Neto .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Teodiano Freire Bastos-Filho

  2. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Eliete Maria de Oliveira Caldeira

  3. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Anselmo Frizera-Neto

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Costa, T.D., Zanella, T.Z., Cristino, C.S., Druzgalski, C., Nogueira-Neto, G.N., Nohama, P. (2022). Analysis of Respiratory EMG Signals for Cough Prediction. In: Bastos-Filho, T.F., de Oliveira Caldeira, E.M., Frizera-Neto, A. (eds) XXVII Brazilian Congress on Biomedical Engineering. CBEB 2020. IFMBE Proceedings, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-70601-2_113

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  • DOI: https://doi.org/10.1007/978-3-030-70601-2_113

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70600-5

  • Online ISBN: 978-3-030-70601-2

  • eBook Packages: EngineeringEngineering (R0)

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